A NEURAL-NETWORK MODEL FOR THE COMPUTATION OF SACCADIC MOTOR ERROR INTHE SUPERIOR COLLICULUS

The deeper layers of the superior colliculus (SC) contain a topographi cal neural map representing the ocular vectorial displacement required for foveation of the target (motor error). After each movement, the m ap is updated by efference copy signals. The process underlying this r emapping was studied in model simulations with a four-layered network having an architecture and access to input signals closely mimicking R obinson's model of the saccadic system. The model shows, for the first time, how the computation of the topographical motor error map in the SC from retinal and eye position signals may proceed in two steps inv olving a stage where target location is coded in a distributed fashion in craniotopic coordinates and a subsequent supracollicular stage whe re radial motor error is represented in a firing-rate code in units wi th broad tuning characteristics. These two stages in the model show in teresting similarities with the characteristics of neuron populations shown neurophysiologically in area 7a and parietal region LIP, respect ively.